Keeping Up With the Fortune 500: Big Data, Predictive Analytics and Healthcare's Next Big Challenge

An oft-cited 2012 exposé in The New York Times revealed the extent of Target's data analytics capabilities. By analyzing customers' purchasing patterns, Target was able to identify, and market to, a major life change — pregnancy.

Charged with identifying pregnant customers to allow for targeted marketing efforts to capture both their baby and household spending, statistician Andrew Pole analyzed purchasing patterns among customers on Target's baby shower registry. He discovered pregnant women in their second trimester purchase items such as unscented lotion and nutritional supplements much more frequently than the general population, patterns which allowed Target to send customers purchasing those items targeted advertisements for baby clothes and supplies.

The story gained notoriety when Target sent baby-related advertisements and coupons to a 17-year-old based on her buying habits, identifying her as pregnant before she had a chance to tell her parents. Target has since denounced the Times article, and has asserted its data collection practices comply with all regulatory standards, including HIPAA.

The Times article shows the potential of data analytics, says Graham Hughes, MD, CMO of SAS Center for Health Analytics and Insights. "It shows that although we have to be aware of privacy issues, predictive analytics gets it right," he says.

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When it comes to data analytics, the healthcare industry is just starting to catch up to other sectors that have been successfully using analytics for decades. "Other industries have spent a lot more time thinking about using analytics for competitive advantages and improved efficiencies," says Dr. Hughes. "But that's the first step of any 12-step program, right? You have to say you have a problem."

Mainly through the widespread adoption and use of electronic medical records, healthcare now has what other industries from retail to banking have long enjoyed: access to large volumes of information about their customer base. "Banks have real-time trading information updated hundreds of times per second, retailers have real-time point of sale information," says Dr. Hughes.

This information is currently being used to make predictions about consumer behavior that can serve as the foundation for business decisions. "A retailer is using data to understand which customer population tends to buy what products at what price sensitivity," says Dr. Hughes. "It's the same in banks; they use as much data as they can to assess your credit risk and determine which financial offerings make the most sense to you."

These industries are able to use incoming data to make these projections because they have spent the past 10 to 15 years investing in the analytics infrastructure to be able to process this type of data, and developing both a team and a strategy to make the most of available information, says Dr. Hughes. "All of the Fortune 500 companies are either using data in this way or embarking on the journey make it happen," he says.

This is a journey healthcare organizations have only just begun. Hospitals and health systems increasingly have access to large amounts of data about their patients, including data from EMRs, billing systems, electronic prescribing systems, health information exchanges and other similar sources. Additionally, the industry as a whole recognizes the potential in this data to provide actionable insights into population health, high-risk patient management, clinical best practices and other business insight that will become increasingly important as the industry moves toward value-based reimbursement.

However, a recent survey conducted by the eHealth Initiative and the College of Healthcare Information Management Executives revealed while 80 percent of CIOs and other healthcare executives believe data analytics are important to their organizations' strategic goals, 84 percent said using big data presents a challenge. Less than half (45 percent) of respondents said their organization has a big data management plan, and just 17 percent reported having staff trained to collect and analyze data.

Further, those organizations that have begun to employ data analytics have not yet seen results. Just 38 percent of hospital and health system leaders recently surveyed by the Society of Actuaries reported seeing no direct business benefits from using big data, though this survey again revealed a self-reported lack of resources and staff to get the most out of the data they have.

Hospital and health systems' struggles to capitalize on data analytics has not gone unnoticed by HIMSS Analytics. Data collected from the country's hospitals by the organization revealed a need for data analytics guidance. "We realized what healthcare organizations were wanting was a maturity model, a framework to understand where they were in regards to analytic maturity, and a roadmap to move forward and apply talent and resources to enhance their analytic abilities and engage in new care-delivery models," says James Gaston, senior director of clinical and business intelligence at HIMSS Analytics.

When setting out to develop the model, Mr. Gaston and his team looked to what other industries were using as analytics guidance. "IT companies, banking and retail have all demonstrated the value of analytics and managing information for effective business use," he says. "So why would we create a whole new model for healthcare when there are good models out there that other industries have embraced?"

HIMSS Analytics worked with The International Institute for Analytics (which was assisted by Dr. Hughes and SAS) to adapt the DELTA Model, an industry-agnostic scale for data analytics maturity described in Analytics at Work1 for use in the healthcare industry. The standards are the same that have helped the aviation, IT, retail and other industries embrace big data analytics, but the language and examples have been tailored for use by a healthcare audience.

The resulting DELTA Poweredtm Analytics Maturity Model focuses on five areas: the availability and accessibility of data, an enterprise approach to data collection, the commitment of leadership to make data-based decisions rather than relying on their personal experience, the organization's focus on the most strategic targets and an analytics staff being developed with the experience to handle the data and with enough career opportunities to keep them in the field.

The model was announced in the spring, and HIMSS Analytics currently has 30 organizations on the model to start developing industry-specific benchmarks. Based on the current industry-agnostic model, Mr. Gaston estimates most healthcare organizations are at about the second of the model's five levels, where data is being collected and used at a very localized level.

"Most organizations have some abilities. Some might be higher, but overall they're just beginning to see the benefits of EMRs and the data they collect," says Mr. Gaston.

For healthcare organizations, this means the time is now to begin more aggressively pursuing a data analytics program, taking advantage of the large amounts of data contained in a modern healthcare system as well as tools and support from industry organizations like HIMSS Analytics.

Some healthcare organizations have already dedicated themselves to getting the most out of their data. Pittsburgh-based UPMC, a system with more than 20 hospitals and more than 4.2 petabytes of data, is actively investing in its data analytics capabilities to deliver the best care to its patient population. However, even Rasu Shrestha, MD, the system's vice president of medical IT, says the system still has a ways to go.

"We have lots of aspirations around predictive models of care, and we're working on correlating clinical data to outcomes data, integrating financial data and data on individual physician performance as well," says Dr. Shrestha, MD. "We're still working to connect all the dots."